A neuroplausible computational model of vision also exhibits asymmetry in developmental category learning

Ankit Gupta Ankit, IIT Kanpur

Devesh Kumar Singh Devesh, IIT Kanpur

Amitabha Mukerjee Mukerjee, IIT Kanpur

Abstract

Computational models are increasingly used to explore possible
mechanisms underlying infant capability in various tasks. Often, such models do
not work on direct image data, but on hand-computed attributes of the images
which are used as input in connectionist models. Such models are open to
criticism since computing intermediate features may knowledge unavailable to the
infant. Here we to explore the feasibility of the Serre-Poggio model which
emulates the cortical ventral stream , and construct infant mental map using
probabilistic models. In experiment 1, we consider asymmetry in visual category
learning in early infancy (e.g. cats vs dogs), and show that surprisal for the
novel category is higher when habituated on cat than on dog. Then we explore the
role of face habituation and hierarchical category in categorization. These
experiments suggest some mechanisms for the internal structures in infant
learning, and also validate the S-P model for such tasks.